code: def predict(
      self, data, batch_size=32, feature_cols=None, profile=False
  ):
      from zoo.orca.data import SparkXShards
  
      param = dict(batch_size=batch_size, profile=profile)
      from pyspark.sql import DataFrame
  
      if isinstance(data, DataFrame):
          xshards, _ = dataframe_to_xshards(
              data,
              validation_data=None,
              feature_cols=feature_cols,
              label_cols=None,
              mode="predict",
          )
          pred_shards = self._predict_spark_xshards(xshards, param)
          result = convert_predict_xshards_to_dataframe(
              data, pred_shards
          )
      elif isinstance(data, SparkXShards):
          if data._get_class_name() == "pandas.core.frame.DataFrame":
              data = process_xshards_of_pandas_dataframe(
                  data, feature_cols
              )
          pred_shards = self._predict_spark_xshards(data, param)
          result = update_predict_xshards(data, pred_shards)
      else:
          raise ValueError(
              "Only xshards or Spark DataFrame is supported for predict"
          )
  
      return result
  
imports: import io
  import types
  import logging
  import numbers
  import torch
  import numpy
  from zoo.orca.data.ray_xshards import RayXShards
  from zoo.orca.learn.pytorch.training_operator import TrainingOperator
  from zoo.orca.learn.pytorch.torch_runner import TorchRunner
  from zoo.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, update_predict_xshards, process_xshards_of_pandas_dataframe
  from zoo.ray import RayContext
  import ray
  from ray.exceptions import RayActorError
label_desc: Use image node_id 7 for calling the PyTorchRayEstimator obj's underlying member method code with example usage: obj.predict(data, batch_size, feature_cols, profile) and returns: result